With the advancement of industrial intelligence, defect recognition has become an indispensable part of facilitating surface quality in the steel manufacturing process. To assure product quality, most previous studies were typically trained with many defect samples. Nonetheless, a large quantity of defect samples is difficult to obtain, owing to the rare occurrence of defects. In general, deep learning-based methods underperformed as they have inherent limitations due to inadequate information, thereby restraining the application of models. In this study, a two-level Gaussian pyramid is applied to decompose raw data into different resolution levels simultaneously filtering the noises to acquire compact and representative features. Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when trained using a lightweight dataset. In addition, the experiments conducted using the disturbance defect dataset showed the robustness of the proposed MRFFN against common noises and motion blur.

Given virtual reality (VR)’s popularity, VR already impacts various applications such as education and tourism. In the above applications, users usually need to follow a specific target, such as a teacher or a tour guide, to explore and learn from the environment. However, because of the constraint with visual senses and unintuitive locomotion, following a target in VR is not as easy as doing it in real life. As a result, the user may not concentrate on the audio narration or the surroundings. Therefore, finding a following mechanism that can help the user concentrate and learn in VR is important. In this paper, we focus on the following techniques in VR. We propose four types of following mechanisms: limited (which restricts the user’s ability to move), semi-limited (which constrains the user’s range of activity), improved semi-limited (with visual assistance), and not limited (user’s movement is not constrained). This study consisted of 29 participants divided into four groups and aimed to evaluate how the following mechanisms affect user experience and make users more concentrated in the virtual world. The experiment shows that the semi-limited following mechanism with visual assistance is superior to the other three types in the performance of the touring experience and helping users pay attention to the tour content.

An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature.

Background/Objectives: To develop a new Virtual Reality (VR) system software for measuring ocular deviation in strabismus patients. Methods: This prospective study included subjects with basic-type exotropia (XT) and non-refractive accommodative esotropia (ET). Ocular deviation was measured using the alternate prism cover test (APCT) and two VR-based methods: target offset (TO) and a newly developed camera rotation (CR) method. Results: A total of 28 subjects were recruited (5 cases were excluded for preliminary testing and 5 for not meeting inclusion criteria). Among the 18 included patients, 10 (66.7%) had XT and 5 (33.3%) had ET. The median age was 21.5 years (IQR 17 to 25). The mean age was 22.3 years (range: 9–46), with 5 (27.8%) having manifest strabismus and 12 (61.1%) measured while wearing glasses. VR-based methods (TO and CR) showed comparable results to APCT for deviation angle measurements (p = 0.604). Subgroup analysis showed no significant differences in ET patients (all p > 0.05). In XT patients, both TO and CR underestimated deviation angles compared to APCT (p = 0.008 and p = 0.001, respectively), but no significant difference was observed between the two methods (p = 0.811). Linear regression showed CR had a stronger correlation with APCT than TO (R² = 0.934 vs. 0.874). Conclusions: This newly developed VR system software, incorporating the CR method, provides a reliable approach for measuring ocular deviation. By shifting the entire visual scene rather than just the target, it lays a strong foundation for immersive diagnostic and therapeutic VR applications.

While many monocular depth estimation methods have been proposed, determining depth variations in outdoor scenes remains challenging. Accordingly, this paper proposes an image segmentation-based monocular depth estimation model with attention mechanisms that can address outdoor scene variations. The segmentation model segments images into foreground and background regions and individually predicts depth maps. Moreover, attention mechanisms are also adopted to extract meaningful features from complex scenes to improve foreground and background depth map prediction via a multi-scale decoding scheme. From our experimental results, we observed that our proposed model outperformed previous methods by $27.5\%$ on the KITTI dataset.